Energy system contributions and determinants of performance in sprint cross-country skiing

被引:52
|
作者
Andersson, E. [1 ]
Bjoerklund, G. [1 ]
Holmberg, H-C. [1 ,2 ]
Ortenblad, N. [1 ,3 ]
机构
[1] Mid Sweden Univ, Dept Hlth Sci, Swedish Winter Sports Res Ctr, Kunskapens Vag 8,Hus D, S-83125 Ostersund, Sweden
[2] Swedish Olymp Comm, Stockholm, Sweden
[3] Univ Southern Denmark, SDU Muscle Res Cluster, Dept Sports Sci & Clin Biomech, Odense, Denmark
关键词
Energetic cost; incline; oxygen demand; oxygen uptake; oxygen deficit; technique transitions; ANAEROBIC CAPACITY; MUSCLE ACTIVATION; DISTANCE RUNNERS; INTENSE EXERCISE; RUNNING ECONOMY; DIAGONAL STRIDE; SKIERS; EFFICIENCY; WORK; COMPETITION;
D O I
10.1111/sms.12666
中图分类号
G8 [体育];
学科分类号
04 ; 0403 ;
摘要
To improve current understanding of energy contributions and determinants of sprint-skiing performance, 11 well-trained male cross-country skiers were tested in the laboratory for VO2max, submaximal gross efficiency (GE), maximal roller skiing velocity, and sprint time-trial (STT) performance. The STT was repeated four times on a 1300-m simulated sprint course including three flat (1 degrees) double poling (DP) sections interspersed with two uphill (7 degrees) diagonal stride (DS) sections. Treadmill velocity and VO2 were monitored continuously during the four STTs and data were averaged. Supramaximal GE during the STT was predicted from the submaximal relationships for GE against velocity and incline, allowing computation of metabolic rate and O-2 deficit. The skiers completed the STT in 232 +/- 10s (distributed as 55 +/- 3% DP and 45 +/- 3% DS) with a mean power output of 324 +/- 26W. The anaerobic energy contribution was 18 +/- 5%, with an accumulated O-2 deficit of 45 +/- 13mL/kg. Block-wise multiple regression revealed that VO2, O-2 deficit, and GE explained 30%, 15%, and 53% of the variance in STT time, respectively (all P<0.05). This novel GE-based method of estimating the O-2 deficit in simulated sprint-skiing has demonstrated an anaerobic energy contribution of 18%, with GE being the strongest predictor of performance.
引用
收藏
页码:385 / 398
页数:14
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